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Fei He and Derek J. Posselt

Abstract

This study advances the understanding of how parameterized physical processes affect the development of tropical cyclones (TCs) in the Community Atmosphere Model (CAM) using the Reed–Jablonowski TC test case. It examines the impact of changes in 24 parameters across multiple physical parameterization schemes that represent convection, turbulence, precipitation, and cloud processes. The one-at-a-time (OAT) sensitivity analysis method quantifies the relative influence of each parameter on TC simulations and identifies which parameters affect six different TC characteristics: intensity, precipitation, longwave cloud radiative forcing (LWCF), shortwave cloud radiative forcing (SWCF), cloud liquid water path (LWP), and ice water path (IWP). It is shown that TC intensity is mainly sensitive to the parcel fractional mass entrainment rate (dmpdz) in deep convection. A decrease in this parameter can lead to a change in simulated intensity from a tropical depression to a category-4 storm. Precipitation and SWCF are strongly affected by three parameters in deep convection: tau (time scale for consumption rate of convective available potential energy), dmpdz, and C0_ocn (precipitation coefficient). Changes in physical parameters generally do not affect LWCF much. In contrast, LWP and IWP are very sensitive to changes in C0_ocn. The changes can be as large as 10 (5) times the control mean value for LWP (IWP). Further examination of the response functions for the subset of most sensitive parameters reveals nonlinear relationships between parameters and most output variables, suggesting that linear perturbation analysis may produce misleading results when applied to strongly nonlinear systems.

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Yanyi He, Kaicun Wang, and Fei Feng

Abstract

Surface incident solar radiation (R s) is important for providing essential information on climate change. Existing studies have shown that the R s values from current reanalyses are significantly overestimated throughout China. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the fifth-generation of atmospheric reanalysis (i.e. ERA5) with a much higher spatiotemporal resolution and a major upgrade than its predecessor, ERA-Interim. This study is to verify whether ERA5 can improve the R s simulation using sunshine duration-derived R s values at ∼2200 stations over China from 1979 to 2014 as reference data. Compared with observed multi-year national mean, the R s overestimation is reduced from 15.88 W·m-2 in ERA-Interim to 10.07 W·m-2 in ERA5. From 1979 to 1993, ERA-Interim (-1.99 W·m-2/decade, p<0.05) and ERA5 (-2.42 W·m-2/decade, p<0.05) estimated R s in China continue to decrease and the latter’s decline is closer to the observed. After 1993, they both show a strong brightening, i.e. 2.26 W·m-2/decade in ERA-Interim and 1.49 W·m-2/decade in ERA5, but observations show a nonsignificant increase by 0.30 W·m-2/decade. Due to the improvement of total cloud cover (TCC) simulation by ERA5, its R s trend bias induced by TCC trend bias is smaller than that in ERA-Interim. In addition, the reason why the simulation trend in ERA5 remains biases might be that ERA5 still ignores aerosol changes on interannual or decadal time scales. Therefore, subsequent reanalysis products still need to improve their simulation of clouds, water vapor and aerosol, especially in aerosol direct and indirect effect on R s.

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Derek J. Posselt, Fei He, Jennifer Bukowski, and Jeffrey S. Reid

Abstract

Monte Carlo and Morris screening techniques are used to examine the relative sensitivity of deep convective simulations to changes in initial conditions (IC) versus changes to microphysical parameters (MP). IC are perturbed using a set of empirical orthogonal function–principal component pairs obtained from a database of tropical soundings, while MP are perturbed consistent with their range of realistic values. Monte Carlo experiments provide a broad overview of parameter–output response, while Morris screening techniques identify the most significant influences on specific model output variables. Changes to MP produce a similar order-of-magnitude response in convective hydrologic cycle, dynamics, and latent heating as changes to IC. Changes in IC appear to have a larger effect on radiative fluxes than perturbations to MP. The distribution of low-level latent heating reveals that changes in MP have a larger influence on cold pool properties than changes to the environment. The dominant effects are produced by a subset of cloud MP and thermodynamic structure functions, indicating perturbation of a subset of the control factors may be used to produce most of the variability in a short-term convective-scale ensemble forecast. The most influential MP are the autoconversion threshold, the rain particle size distribution intercept, and the ice particle fall speed parameters. The most influential EOFs are those that correspond to variability in lower- to midtropospheric temperature and water vapor, as well as zonal low-level shear. The results have implications for both the understanding of what influences convective development and the design of ensemble-based prediction and data assimilation systems.

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Fei He, Derek J. Posselt, Colin M. Zarzycki, and Christiane Jablonowski

Abstract

This paper presents a balanced tropical cyclone (TC) test case designed to improve current understanding of how atmospheric general circulation model (AGCM) configurations affect simulated TC development and behavior. It consists of an analytic initial condition comprising two independently balanced components. The first provides a vortical TC seed, while the second adds a planetary-scale zonal flow with height-dependent velocity and imposes background vertical wind shear (VWS) on the TC seed. The environmental flow satisfies the steady-state hydrostatic primitive equations in spherical coordinates and is in balance with other background field variables (e.g., temperature, surface geopotential). The evolution of idealized TCs in the test case framework is illustrated in 10-day simulations performed with the Community Atmosphere Model, version 5.1.1 (CAM 5.1.1). Environmental wind profiles with different magnitudes, directions, and vertical inflection points are applied to ensure that the technique is robust to changes in the VWS characteristics. The well-known shear-induced intensity change and structural asymmetry in tropical cyclones are well captured. Sensitivity of TC evolution to small perturbations in the initial vortex is also quantitatively addressed to validate the numerical robustness of the technique. It is concluded that the enhanced TC test case can be used to evaluate the impact of model choice (e.g., resolution, physical parameterizations) on the simulation and representation of TC-like vortices in AGCMs.

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Annareli Morales, Derek J. Posselt, Hugh Morrison, and Fei He

Abstract

Microphysical (MP) schemes contain parameters whose values can impact the amount and location of forecasted precipitation, and sensitivity is typically explored by perturbing one parameter at a time while holding the rest constant. Although much can be learned from these “one-at-a-time” studies, the results are limited as these methods do not allow for nonlinear interactions of multiple perturbed parameters. This study applies the Morris one-at-a-time (MOAT) method, a robust statistical tool allowing for simultaneous perturbation of numerous parameters, to explore orographic precipitation sensitivity to changes in microphysical and environmental parameters within an environment characteristic of an atmospheric river. Results show parameters associated with snow fall speed coefficient A s and density ρ s, ice-cloud water collection efficiency (ECI), rain accretion (WRA), relative humidity, zonal wind speed, and surface potential temperature cause the largest influence on simulated precipitation. MP and environmental parameter perturbations can cause precipitation changes of similar magnitude, but results vary by location on the mountain. Different environments are also tested, with A s being the most influential MP parameter regardless of environment. Fewer MP parameters influence precipitation in a faster-wind-speed environment, possibly due to the stronger dynamical forcing upwind and different wave dynamics downwind, compared to a slower-wind-speed environment. Finally, perturbing MP parameters within a single scheme can result in precipitation variations of similar magnitude compared to using entirely different microphysics schemes. MOAT results presented in this study have implications for Bayesian parameter estimation methods and stochastic parameterization within ensemble forecasting.

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Zhuozhuo Lü, Fei Li, Yvan J. Orsolini, Yongqi Gao, and Shengping He

Abstract

It is unclear whether the Eurasian snow plays a role in the tropospheric driving of sudden stratospheric warming (SSW). The major SSW event of February 2018 is analyzed using reanalysis datasets. Characterized by predominant planetary waves of zonal wave 2, the SSW developed into a vortex split via wave–mean flow interaction. In the following two weeks, the downward migration of zonal-mean zonal wind anomalies was accompanied by a significant transition to the negative phase of the North Atlantic Oscillation, leading to extensive cold extremes across Europe. Here, we demonstrate that anomalous Siberian snow accumulation could have played an important role in the 2018 SSW occurrence. In the 2017/18 winter, snow depths over Siberia were much higher than normal. A lead–lag correlation analysis shows that the positive fluctuating snow depth anomalies, leading to intensified “cold domes” over eastern Siberia (i.e., in a region where the climatological upward planetary waves maximize), precede enhanced wave-2 pulses of meridional heat fluxes (100 hPa) by 7–8 days. The snow–SSW linkage over 2003–19 is further investigated, and some common traits among three split events are found. These include a time lag of about one week between the maximum anomalies of snow depth and wave-2 pulses (100 hPa), high sea level pressure favored by anomalous snowpack, and a ridge anchoring over Siberia as precursor of the splits. The role of tropospheric ridges over Alaska and the Urals in the wave-2 enhancement and the role of Arctic sea ice loss in Siberian snow accumulation are also discussed.

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Jingyi Li, Fei Li, Shengping He, Huijun Wang, and Yvan J Orsolini

Abstract

The Tibetan Plateau (TP), referred to as the “Asian water tower”, contains one of the largest land ice masses on Earth. The local glacier shrinkage and frozen-water storage are strongly affected by variations in surface air temperature over the TP (TPSAT), especially in springtime. This study reveals that the relationship between the February North Atlantic Oscillation (NAO) and March TPSAT is unstable with time and regulated by the phase of the Atlantic Multidecadal Variability (AMV). The significant out-of-phase connection occurs only during the warm phase of AMV (AMV+). The results show that during the AMV+, the negative phase of the NAO persists from February to March, and is accompanied by a quasi-stationary Rossby wave train trapped along a northward-shifted subtropical westerly jet stream across Eurasia, inducing an anomalous adiabatic descent that warms the TP. However, during the cold phase of the AMV, the negative NAO can not persist into March. The Rossby wave train propagates along the well-separated polar and subtropical westerly jets, and the NAO−TPSAT connection is broken. Further investigation suggests that the enhanced synoptic eddy and low frequency flow (SELF) interaction over the North Atlantic in February and March during the AMV+, caused by the enhanced and southward-shifted storm track, help maintain the NAO anomaly pattern via positive eddy feedback. This study provides a new detailed perspective on the decadal variability of the North Atlantic−TP connection in late winter−early spring.

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Fei He, Derek J. Posselt, Naveen N. Narisetty, Colin M. Zarzycki, and Vijayan N. Nair

Abstract

This work demonstrates the use of Sobol’s sensitivity analysis framework to examine multivariate input–output relationships in dynamical systems. The methodology allows simultaneous exploration of the effect of changes in multiple inputs, and accommodates nonlinear interaction effects among parameters in a computationally affordable way. The concept is illustrated via computation of the sensitivities of atmospheric general circulation model (AGCM)-simulated tropical cyclones to changes in model initial conditions. Specifically, Sobol’s variance-based sensitivity analysis is used to examine the response of cyclone intensity, cloud radiative forcing, cloud content, and precipitation rate to changes in initial conditions in an idealized AGCM-simulated tropical cyclone (TC). Control factors of interest include the following: initial vortex size and intensity, environmental sea surface temperature, vertical lapse rate, and midlevel relative humidity. The sensitivity analysis demonstrates systematic increases in TC intensity with increasing sea surface temperature and atmospheric temperature lapse rates, consistent with many previous studies. However, there are nonlinear interactions among control factors that affect the response of the precipitation rate, cloud content, and radiative forcing. In addition, sensitivities to control factors differ significantly when the model is run at different resolution, and coarse-resolution simulations are unable to produce a realistic TC. The results demonstrate the effectiveness of a quantitative sensitivity analysis framework for the exploration of dynamic system responses to perturbations, and have implications for the generation of ensembles.

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Xiangrong Yang, Jianfang Fei, Xiaogang Huang, Xiaoping Cheng, Leila M. V. Carvalho, and Hongrang He

Abstract

This study investigates mesoscale convective systems (MCSs) over China and its vicinity during the boreal warm season (May–August) from 2005 to 2012 based on data from the geostationary satellite Fengyun 2 (FY2) series. The authors classified and analyzed the quasi-circular and elongated MCSs on both large and small scales, including mesoscale convective complexes (MCCs), persistent elongated convective systems (PECSs), meso-β circular convective systems (MβCCSs), meso-β elongated convective system (MβECSs), and two additional types named small meso-β circular convective systems (SMβCCSs) and small meso-β elongated convective systems (SMβECSs). Results show that nearly 80% of the 8696 MCSs identified in this study fall into the elongated categories. Overall, MCSs occur mainly at three zonal bands with average latitudes around 20°, 30°, and 50°N. The frequency of MCSs occurrences is maximized at the zonal band around 20°N and decreases with increase in latitude. During the eight warm seasons, the period of peak systems occurrences is in July, followed decreasingly by June, August, and May. Meanwhile, from May to August three kinds of monthly variations are observed, which are clear northward migration, rapid increase, and persistent high frequency of MCS occurrences. Compared to MCSs in the United States, the four types of MCSs (MCCs, PECSs, MβCCSs, and MβECSs) are relatively smaller both in size and eccentricity but exhibit nearly equal life spans. Moreover, MCSs in both countries share similar positive correlations between their duration and maximum extent. Additionally, the diurnal cycles of MCSs in both countries are similar (local time) regarding the three stages of initiation, maturation, and termination.

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Cenlin He, Yoshi Takano, Kuo-Nan Liou, Ping Yang, Qinbin Li, and Fei Chen

Abstract

A set of parameterizations is developed for spectral single-scattering properties of clean and black carbon (BC)-contaminated snow based on geometric-optics surface wave (GOS) computations, which explicitly resolves BC–snow internal mixing and various snow grain shapes. GOS calculations show that, compared with nonspherical grains, volume-equivalent snow spheres show up to 20% larger asymmetry factors and hence stronger forward scattering, particularly at wavelengths <1 μm. In contrast, snow grain sizes have a rather small impact on the asymmetry factor at wavelengths <1 μm, whereas size effects are important at longer wavelengths. The snow asymmetry factor is parameterized as a function of effective size, aspect ratio, and shape factor and shows excellent agreement with GOS calculations. According to GOS calculations, the single-scattering coalbedo of pure snow is predominantly affected by grain sizes, rather than grain shapes, with higher values for larger grains. The snow single-scattering coalbedo is parameterized in terms of the effective size that combines shape and size effects, with an accuracy of >99%. Based on GOS calculations, BC–snow internal mixing enhances the snow single-scattering coalbedo at wavelengths <1 μm, but it does not alter the snow asymmetry factor. The BC-induced enhancement ratio of snow single-scattering coalbedo, independent of snow grain size and shape, is parameterized as a function of BC concentration with an accuracy of >99%. Overall, in addition to snow grain size, both BC–snow internal mixing and snow grain shape play critical roles in quantifying BC effects on snow optical properties. The present parameterizations can be conveniently applied to snow, land surface, and climate models including snowpack radiative transfer processes.

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